Evaluating LLM-corrupted Crowdsourcing Data Without Ground Truth

• URL: http://arxiv.org/abs/2506.06991v1
• Date: Sun, 08 Jun 2025 04:38:39 GMT
• Title: Evaluating LLM-corrupted Crowdsourcing Data Without Ground Truth
• Authors: Yichi Zhang, Jinlong Pang, Zhaowei Zhu, Yang Liu,
• Abstract summary: Large language models (LLMs) by crowdsourcing workers pose a challenge to datasets intended to reflect human input.<n>We propose a training-free scoring mechanism with theoretical guarantees under a crowdsourcing model that accounts for LLM collusion.
• Score: 21.672923905771576
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The recent success of generative AI highlights the crucial role of high-quality human feedback in building trustworthy AI systems. However, the increasing use of large language models (LLMs) by crowdsourcing workers poses a significant challenge: datasets intended to reflect human input may be compromised by LLM-generated responses. Existing LLM detection approaches often rely on high-dimension training data such as text, making them unsuitable for annotation tasks like multiple-choice labeling. In this work, we investigate the potential of peer prediction -- a mechanism that evaluates the information within workers' responses without using ground truth -- to mitigate LLM-assisted cheating in crowdsourcing with a focus on annotation tasks. Our approach quantifies the correlations between worker answers while conditioning on (a subset of) LLM-generated labels available to the requester. Building on prior research, we propose a training-free scoring mechanism with theoretical guarantees under a crowdsourcing model that accounts for LLM collusion. We establish conditions under which our method is effective and empirically demonstrate its robustness in detecting low-effort cheating on real-world crowdsourcing datasets.

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